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Minimum Epsilon-Kernel Computation for Large-Scale Data Processing

Journal of Computer Science & Technology 2022年第6期37卷 1398-1411页

作者：郭鸿杰李建中高宏 School of Computer Science and Technology Harbin Institute of TechnologyHarbin 150001China Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518000China

Kernel is a kind of data summary which is elaborately extracted from a large dataset.Given a problem,the solution obtained from the Kernel is an approximate version of the solution obtained from the whole dataset with a provable approximate ratio.It is widely used in geometric optimization,clustering,and approximate query processing,etc.,for scaling them up to massive data.In this paper,we focus on the minimumε-Kernel(MK)computation that asks for a Kernel of the smallest size for large-scale data processing.For the open problem presented by Wang et al.that whether the minimumε-coreset(MC)problem and the MK problem can be reduced to each other,we first formalize the MK problem and analyze its complexity.Due to the NP-hardness of the MK problem in three or higher dimensions,an approximate algorithm,namely Set Cover-Based Minimumε-Kernel algorithm(SCMK),is developed to solve it.We prove that the MC problem and the MK problem can be Turing-reduced to each other.Then,we discuss the update of MK under insertion and deletion operations,respectively.Finally,a randomized algorithm,called the Randomized Algorithm of Set Cover-Based Minimumε-Kernel algorithm(RA-SCMK),is utilized to further reduce the complexity of SCMK.The efficiency and effectiveness of SCMK and RA-SCMK are verified by experimental results on real-world and synthetic datasets.Experiments show that the Kernel sizes of SCMK are 2x and 17.6x smaller than those of an ANN-based method on real-world and synthetic datasets,respectively.The speedup ratio of SCMK over the ANN-based method is 5.67 on synthetic datasets.RA-SCMK runs up to three times faster than SCMK on synthetic datasets.

关键词： approximate query processing Kernel large-scale dataset NP-hard

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Optimization of optical convolution Kernel of optoelectronic hybrid convolution neural network

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Optoelectronics Letters 2022年第3期18卷 181-186页

作者： XU Xiaofeng ZHU Lianqing ZHUANG Wei ZHANG Dongliang LU Lidan YUAN Pei School of Electro-Optical Engineering Changchun University of Science and TechnologyJilin 130022China Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument Beijing Information Science and Technology UniversityBeijing 100192China Beijing Laboratory of Optical Fiber Sensing and System Beijing Information Science and Technology UniversityBeijing 100016China

To enhance the optical computation’s utilization efficiency, we develop an optimization method for optical convolution Kernel in the optoelectronic hybrid convolution neural network(OHCNN). To comply with the actual calculation process, the convolution Kernel is expanded from single-channel to two-channel, containing positive and negative weights. The Fashion-MNIST dataset is used to test the network architecture’s accuracy, and the accuracy is improved by 7.5% with the optimized optical convolution Kernel. The energy efficiency ratio(EER) of two-channel network is 46.7% higher than that of the single-channel network, and it is 2.53 times of that of traditional electronic products.

关键词： convolution Kernel weights

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Image Manipulation Detection Through Laterally Linked Pixels and Kernel Algorithms

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Computer Systems Science & Engineering 2022年第4期41卷 357-371页

作者： K.K.Thyagharajan G.Nirmala RMD Engineering College KavaraipettaiChennaiIndia

In this paper,copy-move forgery in image is detected for single image with multiple manipulations such as blurring,noise addition,gray scale conver-sion,brightness modiﬁcations,rotation,Hu adjustment,color adjustment,contrast changes and JPEG Compression.However,traditional algorithms detect only copy-move attacks in image and never for different manipulation in single image.The proposed LLP(Laterally linked pixel)algorithm has two dimensional arrays and single layer is obtained through unit linking pulsed neural network for detec-tion of copied region and Kernel tricks is applied for detection of multiple manip-ulations in single forged image.LLP algorithm consists of two channels such as feeding component(F-Channel)and linking component(L channel)for linking pixels.LLP algorithm linking pixels detects image with multiple manipulation and copy-move forgery due to one-to-one correspondence between pixel and neu-ron,where each pixel’s intensity is taken as input for F channel of neuron and connected for forgery identiﬁcation.Furthermore,neuron is connected with neighboringﬁeld of neuron by L channel for detecting forged images with multi-ple manipulations in the image along with copy-move,through Kernel trick clas-siﬁer(KTC).From experimental results,proposed LLP algorithm performs better than traditional algorithms for multiple manipulated copy and paste images.The accuracy obtained through LLP algorithm is about 90%and further forgery detec-tion is improved based on optimized Kernel selections in classiﬁcation algorithm.

关键词： Machine learning copy move forgery support vectors Kernel feature extraction

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Selective Kernel networks for weakly supervised relation extraction

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CAAI Transactions on Intelligence Technology 2021年第2期6卷 224-234页

作者： Ziyang Li Feng Hu Chilong Wang Weibin Deng Qinghua Zhang Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and TelecommunicationsChongqingChina

The purpose of relation extraction is to identify the semantic relations between entities in sentences that contain two entities.Recently,many variants of the convolution neural network(CNN)have been introduced to relation extraction for the extracting of features--the quality of the neural network model directly affects the final quality of relation extraction.However,the traditional convolution network uses a fixed convolution Kernel,so it is difficult to choose the size of the convolution Kernel dynamically,which results in networks with weak representation ability.To address this,a novel CNN is designed with selective Kernel networks and multigranularity.In the process of feature extraction,the model can adaptively select the size of the convolution Kernel,that is,give more weight to the appropriate convolution Kernel.It is then combined with multigranularity convolution to obtain more abundant semantic information.Finally,a new pooling method is designed to obtain more comprehensive information and improve model performance.Experimental results indicate that this method is effective without excessively deep network layers,and it also outperforms several competitive baseline methods.

关键词： Kernel convolution relation

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Dek42 encodes an RNA-binding protein that affects alternative pre-m RNA splicing and maize Kernel development

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Journal of Integrative Plant Biology 2019年第6期61卷 728-748页

作者： Yi Zuo Fan Feng Weiwei Qi Rentao Song State Key Laboratory of Plant Physiology and Biochemistry National Maize Improvement CenterBeijing Key Laboratory of Crop Genetic ImprovementJoint International Research Laboratory of Crop Molecular BreedingCollege of Agronomy and BiotechnologyChina Agricultural UniversityBeijing 100193China Shanghai Key Laboratory of Bio-Energy Crops Plant Science CenterSchool of Life SciencesShanghai UniversityShanghai 200444China

RNA-binding proteins (RBPs) play an important role in post-transcriptional gene regulation. However, the functions of RBPs in plants remain poorly understood. Maize Kernel mutant dek42 has small defective Kernels and lethal seedlings. Dek42 was cloned by Mutator tag isolation and further confirmed by an independent mutant allele and clustered regularly interspaced short palindromic repeats (CRISPR)-CRISPR-associated protein 9 materials. Dek42 encodes an RRM_RBM48 type RNA-binding protein that localizes to the nucleus. Dek42 is constitutively expressed in various maize tissues. The dek42 mutation caused a significant reduction in the accumulation of DEK42 protein in mutant Kernels. RNA-seq analysis showed that the dek42 mutation significantly disturbed the expression of thousands of genes during maize Kernel development. Sequence analysis also showed that the dek42 mutation significantly changed alternative splicing in expressed genes, which were especially enriched for the U12-type intron-retained type. Yeast two-hybrid screening identified SF3a1 as a DEK42-interacting protein. DEK42 also interacts with the spliceosome component U1-70K. These results suggested that DEK42 participates in the regulation of pre-messenger RNA splicing through its interaction with other spliceosome components. This study showed the function of a newly identified RBP and provided insights into alternative splicing regulation during maize Kernel development.

关键词： Dek42 encodes an RNA-binding protein that affects alternative pre-mRNA splicing and maize Kernel development^FA Dek42 encodes

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A primal perspective for indefinite Kernel SVM problem

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Frontiers of Computer Science 2020年第2期14卷 349-363页

作者： Hui XUE Haiming XU Xiaohong CHEN Yunyun WANG School of Computer Science and Engineering Southeast UniversityNanjing 210096China Key Laboratory of Computer Network and Information Integration(Southeast University) MOENanjing 210096China College of Science Nanjing University of Aeronautics and AstronauticsNanjing 210016China School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing 210046China

Indefinite Kernel support vector machine(IKSVM)has recently attracted increasing attentions in machine learning.Since IKSVM essentially is a non-convex problem,existing algorithms either change the spectrum of indefinite Kernel directly but risking losing some valuable information or solve the dual form of IKSVM whereas suffering from a dual gap problem.In this paper,we propose a primal perspective for solving the problem.That is,we directly focus on the primal form of IKSVM and present a novel algorithm termed as IKSVM-DC for binary and multi-class classification.Concretely,according to the characteristics of the spectrum for the indefinite Kernel matrix,IKSVM-DC decomposes the primal function into the subtraction of two convex functions as a difference of convex functions(DC)programming.To accelerate convergence rate,IKSVM-DC combines the classical DC algorithm with a line search step along the descent direction at each iteration.Furthermore,we construct a multi-class IKSVM model which can classify multiple classes in a unified form.A theoretical analysis is then presented to validate that IKSVM-DC can converge to a local minimum.Finally,we conduct experiments on both binary and multi-class datasets and the experimental results show that IKSVM-DC is superior to other state-of-the-art IKSVM algorithms.

关键词： indefinite Kernel support vector machine multi-class classification non-convex optimization

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Modeling turbulent transport effects on Kernel formation and ?ame propagation in an ignition process

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Chinese Journal of Aeronautics 2019年第4期32卷 895-905页

作者： Linghan KONG Zhuyin REN Lingyun HOU School of Aerospace Engineering Tsinghua University

Reliable relighting is crucial for advanced low-emission aero-engine combustors. For forced ignition under highly-turbulent conditions, eddies of sizes being smaller than the flame Kernel can affect its formation through penetrating and modifying the Kernel structure. In this study, a one-dimensional model for small-scale turbulence on Kernel formation is developed through the incorporation of turbulence-induced diffusion in governing equations. With given flow conditions,the spatial and time-dependent turbulent diffusivity is modeled using the idea of residual eddy viscosity. One-dimensional spherical flames of premixed pre-vaporized n-dodecane/air mixtures under high-altitude conditions are simulated to investigate the effects of turbulence. It is revealed that the range of the equivalence ratio for successful turbulent ignition is much narrower than that for laminar ignition. The range decreases with an increased turbulent intensity, and this effect is more pronounced for a low spark energy. In addition, turbulent transport has more pronounced effects on rich mixtures. An analysis on energy budget and species profiles shows that turbulence-induced diffusion enhances the heat loss at the very beginning of the Kernel formation process that could lead to ignition failure. After the flame Kernel is established, turbulent transport broadens the flame front, enhances the heat release rate, and thereafter increases the flame propagation speed.

关键词： Flame Kernel Flame propagation High-altitude ignition Kernel formation probability Turbulence-induced diffusion

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LKLR: A Local Tangent Space-Alignment Kernel Least-Squares Regression Algorithm

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Tsinghua Science and Technology 2019年第4期24卷 389-399页

作者： Chao Tan Genlin Ji the School of Computer Science and Engineering Southeast University the School of Computer Science and Technology Nanjing Normal University the School of Computer Science and Technology Nanjing Normal University

In the fields of machine learning and data mining, label learning is a nascent area of research, and within this paradigm, there is much room for improving multi-label manifold learning algorithms for high-dimensional data. Thus far, researchers have experimented with mapping relationships from the feature space to the traditional logical label space(using neighbors in the label space, for example, to predict logical label vectors from the feature space's manifold structure). Here we combine the feature manifold's and label space's local topological structures to reconstruct the label manifold. To achieve this, we use a nonlinear manifold learning algorithm to transform the local topological structure from the feature space to the label space. Our algorithm adopts a regularized leastsquares Kernel method to realize the reconstruction process, employing an optimization function to find the best solution. Extensive experiments show that our algorithm significantly improves multi-label manifold learning in terms of learning accuracy and time complexity.

关键词： multi-label manifold learning local topological structure regularized least squares Kernel method

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Kernel metabolites depict the diversity of relationship between maize hybrids and their parental lines

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The Crop Journal 2021年第1期9卷 181-191页

作者： Yang Xu Ying Ma Xin Wang Cheng Li Xuecai Zhang Pengcheng Li Zefeng Yang Chenwu Xu Key Laboratory of Plant Functional Genomics of the Ministry of Education Jiangsu Key Laboratory of Crop Genomics and Molecular BreedingJiangsu Co-Innovation Center for Modern Production Technology of Grain CropsCollege of AgricultureYangzhou UniversityYangzhou 225009JiangsuChina International Maize and Wheat Improvement Center(CIMMYT) 06600 MexicoD.F.Mexico

As the end products of cellular regulatory processes,metabolites provide the link between genotypes and phenotypes.Althoughmetabolites have been widely applied for functional gene detection and phenotype prediction in maize,there is little research focusing on the genetic information of metabolites per se.Here,we performed genetic analyses for the Kernel metabolites of 11 parental inbred lines of six representative maize varieties,including Zhongdan 2,Danyu 13,Yedan 13,Zhengdan 958,Xianyu 355,and Suyu 16,as well as their 26 reciprocal hybrids.We identified a total of 208 metabolites in maize Kernels using untargeted metabolite profiling technology.Both cluster analysis and principal component analysis indicated that Kernel metabolites could distinguish hybrids from their parents.Analysis of variance further revealed that 163 metabolites exhibited significant differences between parents and hybrids,and 40 metabolites showed significant differences between reciprocal crosses.We also investigated the genetic effects and heterosis for each metabolite.By taking all hybrids into consideration,about two-thirds of all metabolites displayed overdominant with 36.8%and 31%of them displaying positive overdominant and negative overdominant,respectively.Besides,27.5%and 20.4%of all hybrid combinations showed significant mid-parent heterosis and over-parent heterosis,respectively.Our findings revealed that Kernel metabolites exhibited the diversity of relationship between maize hybrids and their parental lines.Additionally,we identified 25 significant metabolicmarkers related to 11 agronomic traits using the LASSO method.Seven metabolic markers were associated with more than one trait simultaneously.These results provide a genetic basis for further utilization of metabolites in the genetic improvement of maize.

关键词： Maize Kernel Metabolite Reciprocals Heterosis

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Influence of Kernel clustering on an RBFN

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CAAI Transactions on Intelligence Technology 2019年第4期4卷 255-260页

作者： Changming Zhu Duoqian Miao College of Information Engineering Shanghai Maritime UniversityNo.1550Haigang AvenueShanghaiPeople’s Republic of China Department of Computer Science and Technology Tongji UniversityNo.4800Cao’an HighwayShanghaiPeople’s Republic of China

Classical radial basis function network(RBFN)is widely used to process the non-linear separable data sets with the introduction of activation functions.However,the setting of parameters for activation functions is random and the distribution of patterns is not taken into account.To process this issue,some scholars introduce the Kernel clustering into the RBFN so that the clustering results are related to the parameters about activation functions.On the base of the original Kernel clustering,this study further discusses the influence of Kernel clustering on an RBFN when the setting of Kernel clustering is changing.The changing involves different Kernel-clustering ways[bubble sort(BS)and escape nearest outlier(ENO)],multiple Kernel-clustering criteria(static and dynamic)etc.Experimental results validate that with the consideration of distribution of patterns and the changes of setting of Kernel clustering,the performance of an RBFN is improved and is more feasible for corresponding data sets.Moreover,though BS always costs more time than ENO,it still brings more feasible clustering results.Furthermore,dynamic criterion always cost much more time than static one,but Kernel number derived from dynamic criterion is fewer than the one from static.

关键词： Kernel RBFN activation

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